🚔 Crime Rate Predictor – Python / Machine Learning Project
Empower public safety with data-driven foresight. This Crime Rate Predictor uses Python and a Random Forest Regression model to forecast crime rates across 19 major Indian cities. Ideal for developers exploring predictive analytics, smart cities, and real-world data science impact.
Key Features
• Predicts rates for 10 different crime categories (e.g., cybercrimes, crimes against women)
• Covers 19 Indian metropolitan cities with data spanning 2014 to 2021
GitHub
+1
• Built using Random Forest Regression for high accuracy (~93.2%)
GitHub
+1
• User-friendly interface: select city, crime type, and year—get immediate predictions
🔗 Explore the full project here:
Crime Rate Predictor – View Project
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https://t.me/Projectwithsourcecodes
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Empower public safety with data-driven foresight. This Crime Rate Predictor uses Python and a Random Forest Regression model to forecast crime rates across 19 major Indian cities. Ideal for developers exploring predictive analytics, smart cities, and real-world data science impact.
Key Features
• Predicts rates for 10 different crime categories (e.g., cybercrimes, crimes against women)
• Covers 19 Indian metropolitan cities with data spanning 2014 to 2021
GitHub
+1
• Built using Random Forest Regression for high accuracy (~93.2%)
GitHub
+1
• User-friendly interface: select city, crime type, and year—get immediate predictions
🔗 Explore the full project here:
Crime Rate Predictor – View Project
📢 Discover more practical, real-world Python and ML projects:
https://t.me/Projectwithsourcecodes
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Update Gadh
Best Language Detection Web App using Machine Learning & NLP
Language Detection is an advanced machine learning application built to accurately identify and classify the language of any given text.
🌐 Language Detection System – Python Project
Identify the language of any text instantly using Python. A great project for exploring NLP and building multilingual-ready applications.
Key Features
• Detects language from text input with high accuracy
• Supports 50+ global languages
• Uses Python NLP libraries for processing
• Easy-to-understand, modular code suitable for portfolios
🔗 Explore the full project here:
Language Detection System – View Project
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https://t.me/Projectwithsourcecodes
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Identify the language of any text instantly using Python. A great project for exploring NLP and building multilingual-ready applications.
Key Features
• Detects language from text input with high accuracy
• Supports 50+ global languages
• Uses Python NLP libraries for processing
• Easy-to-understand, modular code suitable for portfolios
🔗 Explore the full project here:
Language Detection System – View Project
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Stock Price Prediction with Machine Learning – Professional Django Web App
Stock Price Prediction with Machine Learning is a fully functional web application that provides real-time stock market insights along with accurate
📈 Stock Price Prediction – Python Project
Predict future stock market trends using Python and machine learning. A perfect project for finance, data science, and AI enthusiasts looking to explore predictive analytics.
Key Features
• Real-time stock data fetching and analysis
• Interactive charts for trend visualization
• Predictive model using historical data
• Simple, user-friendly interface for quick insights
🔗 Explore the full project here:
Stock Price Prediction – View Project
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Predict future stock market trends using Python and machine learning. A perfect project for finance, data science, and AI enthusiasts looking to explore predictive analytics.
Key Features
• Real-time stock data fetching and analysis
• Interactive charts for trend visualization
• Predictive model using historical data
• Simple, user-friendly interface for quick insights
🔗 Explore the full project here:
Stock Price Prediction – View Project
📢 Discover more practical Python & ML projects:
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💰 Gold Price Prediction – Python Project
Predict gold price trends using data science and machine learning. Ideal for developers looking to apply forecasting techniques in finance.
Key Features
• Uses historical gold pricing data for modeling
• Employs regression techniques (e.g., Linear Regression, ARIMA)
• Visualizes predicted vs actual price trends
• Built with Python libraries like pandas, scikit-learn, or statsmodels
🔗 Explore the full project here:
Gold Price Prediction – View Project
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Predict gold price trends using data science and machine learning. Ideal for developers looking to apply forecasting techniques in finance.
Key Features
• Uses historical gold pricing data for modeling
• Employs regression techniques (e.g., Linear Regression, ARIMA)
• Visualizes predicted vs actual price trends
• Built with Python libraries like pandas, scikit-learn, or statsmodels
🔗 Explore the full project here:
Gold Price Prediction – View Project
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📊 Hotel Price Prediction – Python Project
Predict hotel room prices using machine learning and historical data. Ideal for developers exploring regression, time-series models, and pricing strategy tools.
Key Features
• Uses historical hotel data to train prediction models
• Applies regression techniques (e.g., Linear Regression, Random Forest, XGBoost)
• Visual comparison of actual vs. predicted price trends
• Built with Python data libraries like pandas, scikit-learn, or statsmodels
🔗 Explore the full project here:
Hotel Price Prediction – View Project
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Predict hotel room prices using machine learning and historical data. Ideal for developers exploring regression, time-series models, and pricing strategy tools.
Key Features
• Uses historical hotel data to train prediction models
• Applies regression techniques (e.g., Linear Regression, Random Forest, XGBoost)
• Visual comparison of actual vs. predicted price trends
• Built with Python data libraries like pandas, scikit-learn, or statsmodels
🔗 Explore the full project here:
Hotel Price Prediction – View Project
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🚲 Bike Sharing Demand Prediction – Python Project
Forecast how many bikes will be rented using historical data and predictive analytics. Ideal for those working on urban mobility, time-series forecasting, or ML in smart cities.
Key Features
• Uses historical bike-sharing data with features like weather, hour, day, season, and holidays
• Applies regression models such as Linear Regression, Random Forest, Gradient Boosting, or XGBoost
• Visualizes actual vs. predicted demand trends
• Built with Python data science libraries like pandas, scikit-learn, and possibly Streamlit or Flask for interface
🔗 Explore the full project here:
Bike Sharing Demand Prediction – View Project
📢 Discover more practical Python & ML projects:
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Forecast how many bikes will be rented using historical data and predictive analytics. Ideal for those working on urban mobility, time-series forecasting, or ML in smart cities.
Key Features
• Uses historical bike-sharing data with features like weather, hour, day, season, and holidays
• Applies regression models such as Linear Regression, Random Forest, Gradient Boosting, or XGBoost
• Visualizes actual vs. predicted demand trends
• Built with Python data science libraries like pandas, scikit-learn, and possibly Streamlit or Flask for interface
🔗 Explore the full project here:
Bike Sharing Demand Prediction – View Project
📢 Discover more practical Python & ML projects:
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📊 Employee Attrition Prediction – Python/ML Project
Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
• Trains models on employee demographics, performance, and engagement metrics
• Predicts attrition risk using algorithms like Logistic Regression or Random Forest
• Provides visual dashboards comparing predicted vs actual turnover trends
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Employee Attrition Prediction – View Project
📢 Discover more practical Python & ML projects:
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Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
• Trains models on employee demographics, performance, and engagement metrics
• Predicts attrition risk using algorithms like Logistic Regression or Random Forest
• Provides visual dashboards comparing predicted vs actual turnover trends
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Employee Attrition Prediction – View Project
📢 Discover more practical Python & ML projects:
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📊 Employee Attrition Prediction – Python/ML Project
Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
• Trains models on employee demographics, performance, and engagement metrics
• Predicts attrition risk using algorithms like Logistic Regression or Random Forest
• Provides visual dashboards comparing predicted vs actual turnover trends
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Employee Attrition Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#EmployeeAttritionPrediction #PythonProject #MachineLearning #DataScience #HRAnalytics #PredictiveModeling #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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Forecast employee turnover using machine learning and HR data. Perfect for developers working on predictive analytics and organizational insights.
Key Features
• Trains models on employee demographics, performance, and engagement metrics
• Predicts attrition risk using algorithms like Logistic Regression or Random Forest
• Provides visual dashboards comparing predicted vs actual turnover trends
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Employee Attrition Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#EmployeeAttritionPrediction #PythonProject #MachineLearning #DataScience #HRAnalytics #PredictiveModeling #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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📉 Hotel Booking Cancellation Prediction – Python/ML Project
Predict hotel booking cancellations using historical reservation data and machine learning techniques. Ideal for developers working on predictive analytics in hospitality and travel sectors.
Key Features
• Analyzes guest profiles, booking channel, stay dates, and special requests
• Uses algorithms like Logistic Regression, Random Forest, or Gradient Boosting
• Visualizes actual vs predicted cancellation trends for clear insight
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Hotel Booking Cancellation Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#HotelBookingCancellationPrediction #PythonProject #MachineLearning #DataScience #HospitalityTech #PredictiveAnalytics #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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Predict hotel booking cancellations using historical reservation data and machine learning techniques. Ideal for developers working on predictive analytics in hospitality and travel sectors.
Key Features
• Analyzes guest profiles, booking channel, stay dates, and special requests
• Uses algorithms like Logistic Regression, Random Forest, or Gradient Boosting
• Visualizes actual vs predicted cancellation trends for clear insight
• Built with Python libraries such as pandas, scikit-learn, Matplotlib/Seaborn
🔗 Explore the full project here:
Hotel Booking Cancellation Prediction – View Project
📢 Discover more practical Python & ML projects:
https://t.me/Projectwithsourcecodes
#HotelBookingCancellationPrediction #PythonProject #MachineLearning #DataScience #HospitalityTech #PredictiveAnalytics #OpenSourceCode #FinalYearProject #projectwithsourcecodes
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📈 Marketing Campaign Demand Prediction – Python/ML Project
Predict the success of marketing campaigns and forecast demand using real business data. Ideal for developers building data-driven tools for sales and marketing strategy.
Key Features
• Analyzes customer demographics, campaign types, and prior purchasing behavior
• Uses regression or classification models like Random Forest, XGBoost, or Logistic Regression
• Visualizes prediction performance with charts comparing actual and forecasted demand
• Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
🔗 Explore the full project here:
Marketing Campaign Demand Prediction – View Project
📢 Discover more practical Python & ML projects:
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Predict the success of marketing campaigns and forecast demand using real business data. Ideal for developers building data-driven tools for sales and marketing strategy.
Key Features
• Analyzes customer demographics, campaign types, and prior purchasing behavior
• Uses regression or classification models like Random Forest, XGBoost, or Logistic Regression
• Visualizes prediction performance with charts comparing actual and forecasted demand
• Built with Python libraries including pandas, scikit-learn, and Matplotlib/Seaborn
🔗 Explore the full project here:
Marketing Campaign Demand Prediction – View Project
📢 Discover more practical Python & ML projects:
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